Method, system, and computer program product for the evaluation of glycemic control in diabetes from self-monitoring data

ABSTRACT

A method, system, and computer program product related to the diagnosis of diabetes, and is directed to predicting the long-term risk of hyperglycemia, and the long-term and short-term risks of severe hypoglycemia in diabetics, based on blood glucose readings collected by a self-monitoring blood glucose device. The method, system, and computer program product pertain directly to the enhancement of existing home blood glucose monitoring devices, by introducing an intelligent data interpretation component capable of predicting both HbA 1c  and periods of increased risk of hypoglycemia, and to the enhancement of emerging continuous monitoring devices by the same features. With these predictions the diabetic can take steps to prevent the adverse consequences associated with hyperglycemia and hypoglycemia.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present invention claims priority from U.S. Provisional PatentApplication Ser. No. 60/193,037 filed Mar. 29, 2000, entitled “Algorithmfor the Evaluation of Glycemic Control in Diabetes From Self-MonitoringData” the entire disclosure of which is hereby incorporated by referenceherein.

US GOVERNMENT RIGHTS

This invention was made with United States Government support underGrant Nos. NIH/NIDDK: RO1 DK 28288 and NIH/NIDDK: RO1 DK 51562, bothawarded by National Institutes of Health. The United States Governmenthas certain rights in the invention.

FIELD OF THE INVENTION

The present system relates generally to Glycemic Control of individualswith diabetes, and more particularly to a computer-based system andmethod for evaluation of predicting glycosylated hemoglobin (HbA_(1c)and HbA₁) and risk of incurring hypoglycemia.

BACKGROUND OF THE INVENTION

Extensive studies, including the Diabetes Control and ComplicationsTrial (DCCT) (See DCCT Research Group: The Effect Of Intensive TreatmentOf Diabetes On The Development And Progression Of Long-TermComplications Of Insulin-Dependent Diabetes Mellitus. New EnglandJournal of Medicine, 329: 978-986, 1993), the Stockholm DiabetesIntervention. Study (See Reichard P, Phil M: Mortality and TreatmentSide Effects During Long-term Intensified Conventional Insulin Treatmentin the Stockholm Diabetes Intervention Study. Diabetes, 43: 313-317,1994), and the United Kingdom Prospective Diabetes Study (See UKProspective Diabetes Study Group: Effect of Intensive Blood GlucoseControl With Metformin On Complications In Patients With Type 2 Diabetes(UKPDS 34). Lancet, 352: 837-853, 1998), have repeatedly demonstratedthat the most effective way to prevent the long term complications ofdiabetes is by strictly maintaining blood glucose (BG) levels within anormal range using intensive insulin therapy.

However, the same studies have also documented some adverse effects ofintensive insulin therapy, the most acute of which is the increased riskof frequent severe hypoglycemia (SH), a condition defined as an episodeof neuroglycopenia which precludes self-treatment and requires externalhelp for recovery (See DCCT Research Group: Epidemiology of SevereHypoglycemia In The Diabetes Control and Complications Trial. AmericanJournal of Medicine, 90: 450-459, 1991, and DCCT Research Group:Hypoglycemia in the Diabetes Control and Complications Trial. Diabetes,46: 271-286, 1997). Since SH can result in accidents, coma, and evendeath, patients and health care providers are discouraged from pursuingintensive therapy. Consequently, hypoglycemia has been identified as amajor barrier to improved glycemic control (Cryer PE: Hypoglycemia isthe Limiting Factor in the Management Of Diabetes. Diabetes Metab ResRev, 15: 42-46, 1999).

Thus, patients with diabetes face a life-long optimization problem ofmaintaining strict glycemic control without increasing their risk ofhypoglycemia. A major challenge related to this problem is the creationof simple and reliable methods that are capable of evaluating bothpatients' glycemic control and their risk of hypoglycemia, and that canbe applied in their everyday environments.

It has been well known for more than twenty years that glycosylatedhemoglobin is a marker for the glycemic control of individuals withDiabetes Mellitus (Type I or Type II). Numerous researchers haveinvestigated this relationship and have found that glycosylatedhemoglobin generally reflects the average BG levels of a patient overthe previous two months. Since in the majority of patients with diabetesthe BG levels fluctuate considerably over time, it was suggested thatthe real connection between integrated glucose control and HbA_(1c)would be observed only in patients known to be in stable glucose controlover a long period of time.

Early studies of such patients produced an almost deterministicrelationship between the average BG level in the preceding 5 weeks andHbA_(1c), and this curvilinear association yielded a correlationcoefficient of 0.98 (See Aaby Svendsen P, Lauritzen T, Soegard U, NerupJ (1982). Glycosylated Hemoglobin and Steady-State Mean Blood GlucoseConcentration in Type 1 (Insulin-Dependent) Diabetes, Diabetologia, 23,403-405). In 1993 the DCCT concluded that HbA_(1c) was the “logicalnominee” for a gold-standard glycosylated hemoglobin assay, and the DCCTestablished a linear relationship between the preceding mean BG andHbA_(1c) (See Santiago J V (1993). Lessons from the Diabetes Control andComplications Trial, Diabetes, 42, 1549-1554).

Guidelines were developed indicating that an HbA_(1c) of 7% correspondsto a mean BG of 8.3 mM (150 mg/dl), an HbA_(1c) of 9% corresponds to amean BG of 11.7 mM (210 mg/dl), and a 1% increase in HbA_(1c)corresponds to an increase in mean BG of 1.7 mM (30 mg/dl, 2). The DCCTalso suggested that because measuring the mean BG directly is notpractical, one could assess a patient's glycemic control with a single,simple test, namely HbA_(1c). However, studies clearly demonstrate thatHbA_(1c) is not sensitive to hypoglycemia.

Indeed, there is no reliable predictor of a patient's immediate risk ofSH from any data. The DCCT concluded that only about 8% of future SHcould be predicted from known variables such-as-the-history of SH, lowHbA_(1c), and hypoglycemia unawareness. One recent review details thecurrent clinical status of this problem, and provides options forpreventing SH, that are available to patients and their health careproviders (See Bolli, G B: How To Ameliorate The Problem of HypoglycemiaIn Intensive As Well As Nonintensive Treatment Of Type I Diabetes.Diabetes Care, 22, Supplement 2: B43-B52, 1999).

Contemporary home BG monitors provide the means for frequent BGmeasurements through Self-Monitoring of BG (SMBG). However, the problemwith SMBG is that there is a missing link between the data collected bythe BG monitors, and HbA_(1c) and hypoglycemia. In other words, thereare currently no reliable methods for evaluating HbA_(1c) andrecognizing imminent hypoglycemia based on SMBG readings (See Bremer Tand Gough D A: Is blood glucose predictable from previous values? Asolicitation for data. Diabetes 48:445-451, 1999).

Thus, an object of this invention is to provide this missing link byproposing three distinct, but compatible, algorithms for evaluatingHbA_(1c) and the risk of hypoglycemia from SMBG data, to be used topredict the short-term and long-term risks of hypoglycemia, and thelong-term risk of hyperglycemia.

The inventors have previously reported that one reason for a missinglink between the routinely available SMBG data and the evaluation ofHbA_(1c) and the risk of hypoglycemia, is that the sophisticated methodsof data collection and clinical assessment used in diabetes research,are infrequently supported by diabetes-specific and mathematicallysophisticated statistical procedures.

Responding to the need for statistical analyses that take into accountthe specific distribution of BG data, the inventors developed asymmetrizing transformation of the blood glucose measurement scale (SeeKovatchev B P, Cox D J, Gonder-Frederick L A and W L Clarke (1997).Symmetization of the Blood Glucose Measurement Scale and ItsApplications, Diabetes Care, 20, 1655-1658) that works as the follows.The BG levels are measured in mg/dl in the United States, and in mmol/L(or mM) in most other countries. The two scales are directly related by18 mg/dl=1 mM. The entire BG range is given in most references as 1.1 to33.3 mM, and this is considered to cover practically all observedvalues. According to the recommendations of the DCCT (See DCCT ResearchGroup (1993) The Effect Of Intensive Treatment of Diabetes On theDevelopment and Progression of Long-Term Complications ofInsulin-Dependent Diabetes Mellitus. New England Journal of Medicine,329, pp 978-986) the target BG range—also known as the euglycemicrange—for a person with diabetes is 3.9 to 10 mM, hypoglycemia occurswhen the BG falls below 3.9 mM, and hyperglycemia is when the BG risesabove 10 mM. Unfortunately, this scale is numerically asymmetric—thehyperglycemic range (10 to 33.3 mM) is wider than the hypoglycemic range(1.1 to 3.9 mM), and the euglycemic range (3.9 to 10 mM) is not centeredwithin the scale. The inventors correct this asymmetry by introducing atransformation, f(BG), which is a continuous function defined on the BGrange [1.1, 33.3], having the two-parameter analytical form:f(BG,α,β)=[(ln(BG))^(α)−β],α,β>0and which satisfies the assumptions:f(33.3,α,β)−f(1.1,α,β) and  A1f(10.0,α,β)=−f(3.9,α,β).  A2

Next, f(.) is multiplied by a third scaling parameter to fix the minimumand maximum values of the transformed BG range at −√{square root over(10)} and √{square root over (10)} respectively. These values areconvenient since a random variable with a standard normal distributionhas 99.8% of its values within the interval [−√{square root over (10)},√{square root over (10)}]. If BG is measured in mmol/l, when solvednumerically with respect to the assumptions A1 and A2, the parameters ofthe function f(BG, α, β) are α=1.026, β=1.861, and the scaling parameteris γ=1.794. If BG is measured in mg/dl instead, the parameters arecomputed to be α=1.084, β=5.381, and γ=1.509.

Thus, when BG is measured in mmol/l, the symmetrizing transformation isf(BG)=1.794[(ln(BG))^(1.026)−1.861]. and when BG is measured in mg/dlthe symmetrizing transformation is f(BG)=1.509[(ln(BG))^(1.084)−5.381].

On the basis of the symmetrizing transformation f(.) the inventorsintroduced the Low BG Index—a new measure for assessing the risk ofhypoglycemia from SMBG readings (See Cox D J, Kovatchev B P, Julian D M,Gonder-Frederick L A, Polonsky W H, Schlundt D G, Clarke W L: Frequencyof Severe Hypoglycemia In IDDM Can Be Predicted From Self-MonitoringBlood Glucose Data. Journal of Clinical Endocrinology and Metabolism,79: 1659-1662, 1994, and Kovatchev B P, Cox D J, Gonder-Frederick L AYoung-Hyman D, Schlundt D, Clarke W L. Assessment of Risk for SevereHypoglycemia Among Adults With IDDM: Validation of the Low Blood GlucoseIndex, Diabetes Care 21:1870-1875, 1998). Given a series of SMBG datathe Low BG Index is computed as the average of 10.f(BG)² taken forvalues of f(BG)<0 and 0 otherwise. Also suggested was a High BG Index,computed in a symmetrical to the Low BG Index manner, however this indexdid not find its practical application.

Using the Low BG Index in a regression model the inventors were able toaccount for 40% of the variance of SH episodes in the subsequent 6months based on the SH history and SMBG data, and later to enhance thisprediction to 46% (See Kovatchev B P, Straume M, Farhi L S, Cox D J:Estimating the Speed of Blood Glucose Transitions and its RelationshipWith Severe Hypoglycemia. Diabetes, 48: Supplement 1, A363, 1999).

In addition, the inventors developed some data regarding HbA_(1c) andSMBG (See Kovatchev B P, Cox D J, Straume M, Farhy L S. Association ofSelf-monitoring Blood Glucose Profiles with Glycosylated Hemoglobin. In:Methods in Enzymology vol. 321: Numerical Computer Methods, Part C,Michael Johnson and Ludvig Brand, Eds., Academic Press, NY; 2000).

These developments became a part of the theoretical background of thisinvention. In order to bring this theory into practice, several keytheoretical components, among other things, as described in thefollowing sections, were added. In particular, three methods weredeveloped for employing the evaluation of HbA_(1c), long-term andshort-term risk for hypoglycemia. The development of these methods was,but not limited thereto, based on detailed analysis of data for 867individuals with diabetes that included more than 300,000 SMBG readings,records of severe hypoglycemia and determinations of HbA_(1c).

The inventors have therefore sought to improve upon the aforementionedlimitations associated with the conventional methods, and therebyprovide simple and reliable methods that are capable of evaluating bothpatients' glycemic control and their risk of hypoglycemia, and that canbe applied in their everyday environments.

SUMMARY OF THE INVENTION

The invention includes a data analysis method and computer-based systemfor the simultaneous evaluation, from routinely collected SMBG data, ofthe two most important components of glycemic control in diabetes:HbA_(1c) and the risk of hypoglycemia. For the purposes of thisdocument, self-monitoring of BG (SMBG) is defined as any method fordetermination of blood glucose at diabetic patients' natural environmentand includes the methods used by contemporary SMBG devices customarilystoring 200-250 BG readings, as well as methods used by emergingcontinuous monitoring technologies. Given this broad definition of SMBG,this invention pertains directly to the enhancement of existing homeblood glucose monitoring devices by introducing an intelligent datainterpretation component capable of predicting both HbA_(1c) and periodsof increased risk of hypoglycemia, as well as to enhancement of futurecontinuous monitoring devices by the same features.

One aspect of the invention includes a method, system, and computerprogram product for evaluating HbA_(1c) from a predetermined period ofcollected SMBG data, for example 4-6 weeks. In one embodiment, theinvention provides a computerized method and system for evaluating theHbA_(1c) of a patient based on BG data collected over a predeterminedduration. The method includes computing weighted deviation toward highblood glucose (WR) and estimated rate of change of blood glucose (Dr)based on the collected BG data; estimating HbA_(1c) using apredetermined mathematical formula based on the computed WR and Dr; andproviding a predetermined confidence interval for classification of saidestimated value of HbA_(1c).

Another aspect of the invention includes a method, system, and computerprogram product for estimating the long-term probability for severehypoglycemia (SH). This method uses SMBG readings from a predeterminedperiod, for example 4-6 weeks, and predicts the risk of SH within thefollowing 6 months. In one embodiment, the invention provides acomputerized method and system for evaluating the long term probabilityfor severe hypoglycemia (SH) of a patient based on BG data collectedover a predetermined duration. The method includes: computing weighteddeviation toward low blood glucose (WL) and estimated rate of fall ofblood glucose in the low BG range (DrDn) based on the collected BG data;estimating the number of future SH episodes using a predeterminedmathematical formula based on the computed WL and DrDn; and defining aprobability of incurring a select number of SH episodes respective tosaid estimated SH episodes.

Still yet another aspect of the invention includes a method, system, andcomputer program product for identifying 24-hour periods (or otherselect periods) of increased risk of hypoglycemia. This is accomplishedthrough the computation of the short-term risk of hypoglycemia usingSMBG readings collected over the previous 24 hours. In one embodiment,the invention provides a computerized method and system for evaluatingthe short term risk for severe hypoglycemia (SH) of a patient based onBG data collected over a predetermined duration. The method includes:computing weighted deviation toward low blood glucose (WL); determiningMax(wl) by calculating maximum value of wl(BG;2); determining risk valueby taking the geometric mean of WL and Max(wl) over the predeterminedduration; providing a predetermined threshold risk value; and comparingthe determined risk value to the threshold risk value.

These three aspects of the invention can be integrated together toprovide continuous information about the glycemic control of anindividual with diabetes, and enhanced monitoring of the risk ofhypoglycemia.

These and other objects, along with advantages and features of theinvention disclosed herein, will be made more apparent from thedescription, drawings and claims that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages of the presentinvention, as well as the invention itself, will be more fullyunderstood from the following description of preferred embodiments, whenread together with the accompanying drawings in which:

FIG. 1 is a flow chart illustrating the method of calculating theestimated HbA_(1c) and predicted HbA_(1c) confidence intervals inaccordance with the present invention.

FIG. 2 is a flow chart illustrating the method of calculating theestimated number of future SH episodes and the associated probabilitythereof in accordance with the present invention.

FIG. 3 is a flow chart illustrating the method of calculating theestimated short term risk of an incurring imminent SH in accordance withthe present invention.

FIG. 4 is graphical representation of a typical BG disturbances observedbefore and after an episode of severe hypoglycemia.

FIG. 5 illustrates action of the method for predicting short-term SH bypresenting 10 weeks of data for Subject A (upper panel) and Subject B(lower panel). SH episodes are marked by triangle; a black line presentsthe risk value. When the risk threshold is crossed, the method indicatesa subsequent high-risk period (gray bar).

FIG. 6 is a functional block diagram for a computer system forimplementation of the present invention.

FIGS. 7-9 are schematic block diagrams of alternative variations of thepresent invention related processors, communication links, and systems.

DETAILED DESCRIPTION OF THE INVENTION

The invention makes possible, but not limited thereto, the creation ofprecise methods for the evaluation of diabetics' glycemic control, andinclude, firmware and software code to be used in computing the keycomponents of the method. The inventive methods for evaluating HbA_(1c),the long-term probability of SH, and the short-term risk ofhypoglycemia, are also validated based on the extensive data collected,as will be discussed later in this document. Finally, the aspects ofthese methods can be combined in structured display or matrix.

Stationary Measures of BG Deviation

According to the inventors' theory of BG symmetrization (See Kovatchev BP, Straume M, Cox D J, Farhi L S. Risk Analysis of Blood Glucose Data: AQuantitative Approach to Optimizing the Control of Insulin DependentDiabetes. J of Theoretical Medicine, 3:1-10, 2001) the natural clinicalcenter of the BG measurement scale is at BG level of 112.5 mg/dl (6.25mmol/l)—a safe euglycemic value for a diabetes patient.

Given this clinical center of the BG scale, the weighted deviations tothe left (towards hypoglycemia) or to the right (towards hyperglycemia)are computed. The degree of weighting of these deviations will berepresented by parameters a and b respectively as follows:wl(BG;a)=10.f(BG)^(a) if f(BG)<0 and 0 otherwise, andwr(BG;b)=10.f(BG)_(b) if f(BG)>0 and 0 otherwise,where f(BG) is the BG symmetrization function presented in thebackground section. The weighting parameter a and b could be different,or the same for the left and right deviations. The inventors' dataanalyses demonstrated that the optimal for practical applicationparameter values are a=2 (which is the parameter value used forcomputation of the Low BG Index) and b=1. Given a series of BG readingsx₁, x₂, . . . x_(n), the average weighted deviations to the left and tothe right of the clinical center of the BG scale are defined as:${WL} = {{\frac{1}{n}{\sum\limits_{i = 1}^{n}\quad{{wl}\quad\left( {x_{i};2} \right)\quad{and}\quad{WR}}}} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\quad{{wr}\quad\left( {x_{i};1} \right)}}}}$These two measures of BG deviation do not depend on the timing of the BGreadings, and therefore are stationary. In order to capture the dynamicsof BG change, measures of the BG rate of change are introduced asprovided below.

Computation of BG Risk Rate of Change

Let x₁, x₂, . . . x_(n) be n SMBG readings of a subject recorded at timepoints t₁, t₂, . . . t_(n). This data is next transformed by calculatingthe numbers f(x₁), f(x₂,), . . . , f(x_(n)) and draw a cubic spline S(t)passing through the points (t₁,f(x₁)), (t₂,f(x_(2,))), . . . ,(t_(n),f(x_(n))). Thus, the function S(t) is a continuous functiondefined on the whole interval [t₁, t_(n)] and such thatS(t_(j))=f(x_(j)), for j=1, . . . , n. Also calculated are the set ofnumbers s_(k)=10.S(k+t₁)² for k=0, 1, . . . , t_(n)−t₁ thus gettinginterpolated values at one-hour increments.

Next, consider all couples of numbers s_(k) with consecutive indices:C₀=(s₀,s₁), C₁=(s₁,s₂), C₂=(s₂,s₃), . . . and denote by M_(up) the setof all couples C_(k), such that s_(k)>s_(k+1) and by M_(dn) the set ofall couples C_(k), such that s_(k)<s_(k+1).

Finally, let DrDn be the average of the numbers s_(k+1)−s_(k), providedthat C_(k)εM_(dn), and Dr be the average of the numbers s_(k+1)−s_(k),provided that C_(k)εM_(up)+M_(dn).

The numbers DrDn and Dr provide a measure for the rate of change of BGin a “risk space,” e.g. the rate of change of the risk associated withany BG level change. In addition, DrDn measures the rate of BG changeonly when BG goes down, i.e. DrDn evaluates how quickly the risk couldincrease when BG falls, while Dr is a measure of the overallvulnerability of BG to fluctuations. It is further asserted that DrDnwill be associated with risk for hypoglycemia (if someone's bloodglucose could fall quickly, his/her risk for hypoglycemia would behigher), while Dr will be associated with the overall stability of BG.

Software Code (Presented in SPSS Control Language)

The first is for when the BG readings are in mmol/L, and in this casethe variable is BGMM. The second is for when the BG readings are inmg/dl, and in this case the variable is BGMG.

If BG is measured in mmol/L, each BG reading is first transformed asfollows:SCALE1=(ln(BGMM))**1.026−1.861RISK1=32.185*SCALE1*SCALE1

If BG is measured in mg/dl, each BG reading is first transformed asfollows:SCALE2=(ln(BGMG))**1.08405−5.381RISK2=22.765*SCALE2*SCALE2

Further, the left and right weighted deviations are computed as follows:WL=0WL=0IF (SCALE1 le 0.0) WL=RISK1WR=0IF (SCALE1 gt 0.0) WR=sqrt(RISK1)

Provided that the BG readings are equally spaced in time, or areinterpolated at one-hour increments, the BG rate of change is computedas:Dr=RISK1(BG)−RISK1(BG−1)DrDn=0IF(SCALE le 0.0 and Dr gt 0) DrDn=Dr

Finally, an aggregation pass through all BG readings for a subject willproduce:WL=mean(WL)WR=mean(WH)Dr=mean(Dr), and DrDn=mean(DrDn)

Method for the Evaluation of HbA_(1c)

A preferred embodiment of HbA_(1c) evaluation method 100 according tothe invention is illustrated in FIG. 1. In a first step 102, SMBG datais collected over a predetermined period of time. For example, the SMBGdata is collected over 4-6 weeks with a frequency of 3-5 BG measurementsper day, of which are transformed by the code or formulas presented inthe previous section. Different formulas are to be used if the BGmeasurements are stored in mg/dl, or in mmol/l. One skilled in the artwould appreciate that various levels, durations, and frequencies can beemployed. In a step 104, weighted deviation towards high blood glucose(WR) and estimated rate of change of blood glucose (Dr) is computedusing the formula/code discussed above. In a step 106, an estimate ofHbA_(1c) from self-monitoring data is computed using the linearfunction: EstHBA1c=0.9008*WR−0.8207*DR+6.7489. It is noted that thecoefficients of this function are derived from data for 867 individualswith diabetes, and one would recognize that further data accumulationmay update these coefficients. In step 108 HbA_(1c) estimate categoriesrepresenting a range of values for estimated HbA_(1c) are definedaccording to Table 1. TABLE 1 Defining categories on the basis ofEstHBA1c: EstHBA1c <7.8 7.8-8.5 8.5-9.0 9.0-9.6 9.6-10.3 10.3-11.0 >11.0Category 1 2 3 4 5 6 7

In step 110 predicted confidence intervals for corresponding HbA_(1c)are derived according to Table 2. TABLE 2 Predicted 95% confidenceintervals for classification of HbA_(1c): Category 1 2 3 4 5 6 7HBA_(1c) <8.0 8.0-8.5 8.5-9.0 9.0-9.5 9.5-10.1 10.1-11.0 >11.0

In step 112, the estimated HbA_(1c) from step 106 is assigned in one ofthe categories provided in Table 1 and/or Table 2.

Empirical Validation of Evaluation of HbA_(1c)

The intervals for HbA_(1c) in Table 2 are based on extensive research.To validate these intervals we analyzed SMBG and HbA_(1c) data from 867subjects with diabetes. All subjects were instructed to use BG memorymeters for six months and to measure their BG two to four times a day.During the same period 5 to 8 HbA_(1c) assays were performed for eachsubject. The memory meter data were electronically downloaded and storedin a computer for further analysis. This procedure produced a databasecontaining more than 300,000 SMBG readings and 4,180 HbA_(1c) assaystaken over six months. Analysis of variance was conducted to compareHbA_(1c) in the seven categories identified in Table 1. The fivecategories were highly significantly different, with F=91 and p<0.00001.Moreover, the average HbA_(1c) was significantly different for each pairof categories as demonstrated by Duncan's ranges, with p<0.01.

Also, 95% confidence intervals were computed for the mean value ofHbA_(1c) in each of the seven categories. These confidence intervalswere used as a basis for computing the HbA_(1c) intervals presented inTable 2. Post-hoc analysis of the classification power of this methoddemonstrated that the method was well protected against extreme errorssuch as incorrectly classifying HbA_(1c) in category 1, 2 or 3 on thebasis of SMBG while the actual HbA_(1c) was greater than 9.5%, orclassifying HbA_(1c) in category 5, 6 or 7 while the actual HbA_(1c) wasbelow 9.0%.

In summary, after an initial 4-6 weeks of SMBG readings the computerizedmethod computes an interval estimate for the value of HbA_(1c) that canbe used to track patients' changes in glycemic control in the high BGrange.

Method for Evaluation of the Long-Term Probability for SevereHypoglycemia (SH)

A preferred embodiment of long-term probability for SH evaluation method200 according to the invention is illustrated in FIG. 2. In a first step202, SMBG data is collected over a predetermined period of time. Forexample, the SMBG data is collected over 4-6 weeks with a frequency of3-5 BG measurements per day, of which are transformed by the code orformulas presented immediately above. Different formulas are to be usedif the BG measurements are stored in mg/dl, or in mmol/l; One skilled inthe art would appreciate that various levels, durations, and frequenciescan be employed. In a step 204, WL and DrDn are computed using theformula/code as discussed above. In step 206, an estimate of the numberof future SH episodes is computed using the linear function:EstNSH=3.3613*WL−4.3427*DrDn−1.2716.

It is noted that the coefficients of this function are derived from datafor 181 individuals with diabetes, and one would appreciate that furtherdata accumulation may update these coefficients. It is further notedthat this formula provides a single value estimate for the number offuture SH episodes and that through additional methodologies, asdiscussed below, categories are provided with ranges and confidencelevels for enhanced clinical applications. In step 208, estimated numberof SH episodes (estNSH) categories representing a range of values forestNSH are defined according to Table 3. TABLE 3 Classification ofEstNSH: EstNSH <0.775 0.775-3.750 3.750-7.000 >7.000 Category 1 2 3 4

In step 210, respective to the estNSH categories, the probability ofincurring 0, 1-2, or more than 2 SH episodes in the following six monthsis derived, as represented in table 4. TABLE 4 Probability for 0, 1-2,or 2 or more SH episodes in the subsequent 6 months: Category 1 Category2 Category 3 Category 4 0 SH 90% 50% 25% <20% 1-2 SH 10% 25% 25% >2 SH25% 50% >80%

In step 212, the EstNSH from step 206 is assigned in one of thecategories provided in Table 3 and/or Table 4.

Empirical Validation of Evaluation of the Long-Term Probability for SH

One-hundred-eighty-one adults with Type 1 diabetes (mean age 37 years,duration of diabetes 18 years) used memory meters to collect more than34,000 SMBG over a month. Then for the next six months they recorded indiaries any occurrence of SH. The SMBG data were mathematicallytransformed and an a linear regression model was used to predict futuresevere hypoglycemia resulting in a highly significant model (F=36.3,p<0.0001) and multiple R of 55%.

All subjects were classified into 4 categories using the presentlong-term SH method. The average number of future SH episodes incategories 1, 2, 3, and 4 was 0.3, 2.0, 5.0, and 9.75 respectively.Analysis of variance demonstrated highly significant differences betweenthese categories, F=19.0, p<0.0001.

In summary, a linear combination of the Low BG Index and the rate ofdrop of BG as measured in “risk space” provide an accurate assessment ofthe long-term risk of SH. Because it is based on SMBG records that areautomatically stored by many reflectance meters, this is an effectiveand clinically useful indicator of patients' glycemic control in the lowBG range.

Method for the Evaluation of the Short-Term (within 24 hours) Risk ofHypoglycemia

A preferred embodiment of short term risk of SH evaluation method 300according to the invention is illustrated in FIG. 3. In a first step302, SMBG is data is collected over a predetermined short term period.For example, the SMBG data is collected over a 24 hour period, with afrequency of 3-5 BG measurements per day −4 or more readings, as anominal level according to data analyses. One skilled in the art wouldappreciate that various levels, periods (durations), and frequencies canbe employed. In a step 304 WL(24) and Max(wl) is computed from allreadings collected within the preceding 24 hours, wherein the maximumvalue of wl(BG;2) is Max(wl). In step 306, the risk value is by takingthe geometric mean of WL and Max(wl) over the 24 hour period, whereinsaid risk value is mathematically defined as:Risk(24)=√{square root over (WL(24)·Max(wl))};

In step 308 a threshold risk value is determined. In step 310 theestimated risk value is compared to the threshold risk value. Forexample, if the threshold risk value is set at 17, then if Risk(24)>17,then—based on the SMBG data collected over the previous 24 hours—theresultant indication is a high risk of the patient incurring imminenthypoglycemia. In other words, this is a decision-making rule thatconsiders a 24-hour period of SMBG data and judges whether this periodis likely to precede an imminent hypoglycemia episode. The thresholdvalue of 17 is derived from an extensive data set, however, it isrecognized that it is possible that this value maybe adjusted withfurther accumulation of data or for additional objectives.

Empirical Validation of Evaluation of the Short-Term Risk ofHypoglycemia:

Eighty-five individuals were recruited through advertisement innewsletters, diabetes clinics, and through direct referrals. Theinclusion criteria were: 1) age of 21-60 years; 2) type I diabetes withat least two years duration, and insulin use since the time ofdiagnosis; 3) at least 2 documented SH episodes in the past year; and 4)routine use of SMBG devices for diabetes monitoring. The participantswere instructed to use the meter 3-5 times a day, and to record inmonthly diaries any SH episodes, including the exact dates and times oftheir occurrences. SH was defined as severe neuroglycopenia that resultsin stupor or unconsciousness and precludes self-treatment. For eachsubject the study continued 6-8 months and each month the subject'smeter was downloaded and the SH diary was collected. The memory capacityof the meters was sufficient, and the downloading was often enough, sothat no BG data were lost. No changes were made in the participants'diabetes management routine, nor were any additional treatmentsadministered during the study.

During the study a total of 75,495 SMBG readings (on average 4.0±1.5 persubject per day) were downloaded from the participants' memory meters,and 399 (4.7±6.0 per subject) SH episodes were recorded in theirdiaries. An important finding, among other things, was that episodes ofmoderate or severe hypoglycemia are preceded and followed by measurableBG disturbances. In the 24-hour period before an SH episode the Low BGIndex (e.g. WL) rose (p<0.001), the average BG was lower (p=0.001), andthe BG variance increased (p=0.001). In the 24 hours following the SHepisode, the Low BG Index and BG variance remained elevated (p<0.001),but the average BG returned to its baseline.

To this end, FIG. 4 is graphical representation of a typical BGdisturbance observed before and after an episode of severe hypoglycemia.In the period 48 to 24 hours before the SH episode, the average BG leveldecreased and the variance of BG increased. In the 24-hour periodimmediately preceding the SH episode, the average BG level droppedfurther and the variance of BG continued to increase. In the 24-hourperiod following the SH episode, the average BG level normalized, butthe BG variance remained greatly increased. Both the average BG and itsvariance returned to their baseline levels within 48 hours after the SHepisode.

As such, as part of the invention, the disturbances presented in FIG. 4are quantified from SMBG data to enable the evaluation of the short-termrisk of hypoglycemia. The cutoff value of Risk(24)=17 is derived from anoptimization along the following restrictions: 1) the method had topredict a maximum percentage of SH episodes, i.e. to identify as risky amaximum percentage of 24-hour periods preceding SH, and 2) to preventoverestimation of the risk, the method had to identify as risky no morethat 15% of the total time of the study (one day a week on average). Thecutoff risk value of 17 was held constant for all subjects. The reasonfor choosing the value of 15% was to prevent the patients from becomingirritated with an overabundance of “false alarms” and then ignoring“true alarms.” In practice, a patient's physician can select analternate value depending on the severity of the patient's diabetes andparticular objectives.

The following example illustrates the action of the algorithm on theSMBG data of two participants in the study. FIG. 5 presents ten weeks ofdata for Subject A (upper panel) and Subject B (lower panel). SHepisodes are marked by triangles; a black curve presents the risk value.When the risk threshold (the horizontal line at Risk=17) is crossed, thealgorithm indicates a subsequent high-risk period (gray bar). ForSubject A, 7 out of 9 SH episodes are predicted and there are 5 falsealarms, e.g. high-risk periods that did not result in SH; for Subject Bthere are 3 false alarms and the only SH episode is predicted. It isobvious that Subject B's risk values when compared to Subject A's riskvalues, include more and higher deviations. For both subjects, all SHepisodes were accompanied by supercritical risk values, and about halfof all large deviations were accompanied by one or more SH episode.

Across all participants in the study, 44% of all recorded SH episodeswere preceded, within 24 hours, by a high-risk period, and 50% werepreceded, within 48 hours, by a high-risk period. If only periods witheither at least 3, or at least 4 SMBG measurements were considered, theaccuracy of the latter prediction increased to 53% and 57%,respectively. Post-hoc analysis of BG levels occurring during, orimmediately after, high-risk periods that were not followed by an SHepisode, i.e. during, or immediately after false alarms, demonstratedthat the average per subject minimum of such BG levels was 2.3±0.2mmol/l versus 5.9±1.7 mmol/l (t=19.5, p<0.0001) for all non-riskperiods, including all SH episodes that remained unaccounted for. Thisindicates that, although symptomatic SH did not occur, BG levelsfollowing high-risk periods were notably low.

In summary, the inventors simulated the action of the short-term riskmethod on a 6-month series of SMBG readings for 85 individuals with TypeI diabetes. With four or more SMBG readings per day, at least 50% of allepisodes of SH could be anticipated. Even when symptomatic SH did notoccur, the algorithm predicted episodes of moderate hypoglycemia.

Integration of the Three Methods

The three methods of this invention, as discussed above and illustratedin FIGS. 1-3, utilize the same series of SMBG data. Therefore, from anSMBG-device point of view, a unified display or matrix of the results ofthese three methods could be made similar to the grid output presentedbelow: EstHBA categories (Algorithm 1) 1 2 3 4 5 6 7 EstNSH 1 Ss 1 cate-2 gories 3 4 Ss 2

Thus, for example, the output for subject 1 (Ss 1) shown in the abovegrid indicates that this person is likely to have HbA_(1c) between 9 and9.5%, and has a 90% chance not to experience severe hypoglycemia in thesubsequent 6 months. The output for subject 2 (Ss 2) indicates that thisperson is likely to have HbA_(1c) below 8%, and has a greater than 80%chance to experience at least 3 SH episodes in the subsequent 6 months.

In addition to this grid-output, the short term risk method provides acontinuous tracking of the risk of imminent hypoglycemia and can be usedto sound an alarm when this risk becomes high.

The method of the invention may be implemented using hardware, softwareor a combination thereof and may be implemented in one or more computersystems or other processing systems, such as personal digit assistants(PDAs). In an example embodiment, the invention was implemented insoftware running on a general purpose computer 900 as illustrated inFIG. 6. Computer system 600 includes one or more processors, such asprocessor 604. Processor 604 is connected to a communicationinfrastructure 606 (e.g., a communications bus, cross-over bar, ornetwork). Computer system 600 includes a display interface 602 thatforwards graphics, text, and other data from the communicationinfrastructure 606 (or from a frame buffer not shown) for display on thedisplay unit 630.

Computer system 600 also includes a main memory 608, preferably randomaccess memory (RAM), and may also include a secondary memory 610. Thesecondary memory 610 may include, for example, a hard disk drive 612and/or a removable storage drive 614, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, etc. The removable storagedrive 614 reads from and/or writes to a removable storage unit 618 in awell known manner. Removable storage unit 618, represents a floppy disk,magnetic tape, optical disk, etc. which is read by and written to byremovable storage drive 614. As will be appreciated, the removablestorage unit 618 includes a computer usable storage medium having storedtherein computer software and/or data.

In alternative embodiments, secondary memory 610 may include other meansfor allowing computer programs or other instructions to be loaded intocomputer system 600. Such means may include, for example, a removablestorage unit 622 and an interface 620. Examples of such removablestorage units/interfaces include a program cartridge and cartridgeinterface (such as that found in video game devices), a removable memorychip (such as a ROM, PROM, EPROM or EEPROM) and associated socket, andother removable storage units 622 and interfaces 620 which allowsoftware and data to be transferred from the removable storage unit 622to computer system 600.

Computer system 600 may also include a communications interface 624.Communications interface 624 allows software and data to be transferredbetween computer system 600 and external devices. Examples ofcommunications interface 624 may include a modem, a network interface(such as an Ethernet card), a communications port, a PCMCIA slot andcard, etc. Software and data transferred via communications interface624 are in the form of signals 628 which may be electronic,electromagnetic, optical or other signals capable of being received bycommunications interface 624. Signals 628 are provided to communicationsinterface 624 via a communications path (i.e., channel) 626. Channel 626carries signals 628 and may be implemented using wire or cable, fiberoptics, a phone line, a cellular phone link, an RF link and othercommunications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as removablestorage drive 914, a hard disk installed in hard disk drive 612, andsignals 628. These computer program products are means for providingsoftware to computer system 600. The invention includes such computerprogram products.

Computer programs (also called computer control logic) are stored inmain memory 608 and/or secondary memory 610. Computer programs may alsobe received via communications interface 624. Such computer programs,when executed, enable computer system 600 to perform the features of thepresent invention as discussed herein. In particular, the computerprograms, when executed, enable processor 604 to perform the functionsof the present invention. Accordingly, such computer programs representcontrollers of computer system 600.

In an embodiment where the invention is implemented using software, thesoftware may be stored in a computer program product and loaded intocomputer system 600 using removable storage drive 614, hard drive 612 orcommunications interface 624. The control logic (software), whenexecuted by the processor 604, causes the processor 604 to perform thefunctions of the invention as described herein.

In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine to perform the functions described herein will be apparentto persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using acombination of both hardware and software.

In an example software embodiment of the invention, the methodsdescribed above were implemented in SPSS control language, but could beimplemented in other programs such as, but not limited to, C++programming language.

FIGS. 7-9 show block diagrammatic representation of alternativeembodiments of the invention. Referring FIG. 7, there is shown a blockdiagrammatic representation of the system 710 essentially comprises theglucose meter 728 used by a patient 712 for recording, inter alia,insulin dosage readings and measured blood glucose (“BG”) levels, Dataobtained by the glucose meter 728 is preferably transferred throughappropriate communication links 714 or data modem 732 to a processingstation or chip, such as a personal computer 740, PDA, or cellulartelephone. For instance, data stored may be stored within the glucosemeter 728 and may be directly downloaded into the personal computer 740through an appropriate interface cable. An example is the ONE TOUCHmonitoring system or meter by LifeScan, Inc. which is compatible with INTOUCH software which includes an interface cable to down load the datato a personal computer.

The glucose meter is common in the industry and includes essentially anydevice that can functions as a BG acquisition mechanism. The BG meter oracquisition mechanism, device, tool, or system includes variousconventional methods directed toward drawing a blood sample (e.g. byfingerprick) for each test, and a determination of the glucose levelusing an instrument that reads glucose concentrations byelectromechanical or claorimetric methods. Recently, various methods fordetermining the concentration of blood analytes without drawing bloodhave been developed. For example, U.S. Pat. No. 5,267,152 to Yang et al.describes a noninvasive technique of measuring blood glucoseconcentration using near-IR radiation diffuse-reflection laserspectroscopy. Similar near-IR spectrometric devices are also describedin U.S. Pat. No. 5,086,229 to Rosenthal et al. and U.S. Pat. No.4,975,581 to Robinson et al.

U.S. Pat. No. 5,139,023 to Stanley describes a transdermal blood glucosemonitoring apparatus that relies on a permeability enhancer (e.g., abile salt) to facilitate transdermal movement of glucose along aconcentration gradient established between interstitial fluid and areceiving medium. U.S. Pat. No. 5,036,861 to Sembrowich describes apassive glucose monitor that collects perspiration through a skin patch,where a cholinergic agent is used to stimulate perspiration secretionfrom the eccrine sweat gland. Similar perspiration collection devicesare described in U.S. Pat. No. 5,076,273 to Schoendorfer and U.S. Pat.No. 5,140,985 to Schroeder.

In addition, U.S. Pat. No. 5,279,543 to Glikfeld describes the use ofiontophoresis to noninvasively sample a substance through skin into areceptacle on the skin surface. Glikfeld teaches that this samplingprocedure can be coupled with a glucose-specific biosensor orglucose-specific electrodes in order to monitor blood glucose. Moreover,International Publication No. WO 96/00110 to Tamada describes aniontophoretic apparatus for transdermal monitoring of a targetsubstance, wherein an iontophoretic electrode is used to move an analyteinto a collection reservoir and a biosensor is used to detect the targetanalyte present in the reservoir. Finally, U.S. Pat. No. 6,144,869 toBerner describes a sampling system for measuring the concentration of ananalyte present.

Further yet, the BG meter or acquisition mechanism may includeindwelling catheters and subcutaneous tissue fluid sampling.

The computer or PDA 740 includes the software and hardware necessary toprocess, analyze and interpret the self-recorded diabetes patient datain accordance with predefined flow sequences (as described above indetail) and generate an appropriate data interpretation output.Preferably, the results of the data analysis and interpretationperformed upon the stored patient data by the computer 740 are displayedin the form of a paper report generated through a printer associatedwith the personal computer 740. Alternatively, the results of the datainterpretation procedure may be directly displayed on a video displayunit associated with the computer 740.

FIG. 8 shows a block diagrammatic representation of an alternativeembodiment having a diabetes management system that is apatient-operated apparatus 810 having a housing preferably sufficientlycompact to enable apparatus 810 to be hand-held and carried by apatient. A strip guide for receiving a blood glucose test strip (notshown) is located on a surface of housing 816. Test strip is forreceiving a blood sample from the patient 812. The apparatus includes amicroprocessor 822 and a memory 824 connected to microprocessor 822.Microprocessor 22 is designed to execute a computer program stored inmemory 824 to perform the various calculations and control functions asdiscussed in great detail above. A keypad 816 is connected tomicroprocessor 822 through a standard keypad decoder 826. Display 814 isconnected to microprocessor 822 through a display driver 830.Microprocessor 822 communicates with display driver 830 via aninterface, and display driver 830 updates and refreshes display 814under the control of microprocessor 822. Speaker 854 and a clock 856 arealso connected to microprocessor 822. Speaker 854 operates under thecontrol of microprocessor 822 to emit audible tones alerting the patientto possible future hypoglycemia. Clock 856 supplies the current date andtime to microprocessor 822.

Memory 824 also stores blood glucose values of the patient 812, theinsulin dose values, the insulin types, and the parameter values used bymicroprocessor 822 to calculate future blood glucose values,supplemental insulin doses, and carbohydrate supplements. Each bloodglucose value and insulin dose value is stored in memory 824 with acorresponding date and time. Memory 824 is preferably a non-volatilememory, such as an electrically erasable read only memory (EEPROM).

Apparatus 810 also includes a blood glucose meter 828 connected tomicroprocessor 822. Glucose meter 828 is designed to measure bloodsamples received on blood glucose test strips and to produce bloodglucose values from measurements of the blood samples. As mentionedpreviously, such glucose meters are well known in the art. Glucose meter828 is preferably of the type which produces digital values which areoutput directly to microprocessor 822. Alternatively, blood glucosemeter 828 may be of the type which produces analog values. In thisalternative embodiment, blood glucose meter 828 is connected tomicroprocessor 822 through an analog to digital converter (not shown).

Apparatus 810 further includes an input/output port 834, preferably aserial port, which is connected to microprocessor 822. Port 834 isconnected to a modem 832 by an interface, preferably a standard RS232interface. Modem 832 is for establishing a communication link betweenapparatus 810 and a personal computer 840 or a healthcare providercomputer 838 through a communication network 836. Specific techniquesfor connecting electronic devices through connection cords are wellknown in the art. Another alternative example is “bluetooth” technologycommunication.

Alternatively, FIG. 9 shows a block diagrammatic representation of analternative embodiment having a diabetes management system that is apatient-operated apparatus 910, similar as shown in FIG. 8, having ahousing preferably sufficiently compact to enable the apparatus 910 tobe hand-held and carried by a patient. However, the present embodimentincludes a separate or detachable glucose meter or BG acquisitionmechanism 928.

Accordingly, the embodiments described herein are capable of beingimplemented over data communication networks such as the internet,making evaluations, estimates, and information accessible to anyprocessor or computer at any remote location, as depicted in FIGS. 6-9and/or U.S. Pat. No. 5,851,186 to Wood, of which is hereby incorporatedby reference herein. Alternatively, patients located at remote locationsmay have the BG data transmitted to a central healthcare provider orresidence, or a different remote location.

In summary, the invention proposes a data analysis computerized methodand system for the simultaneous evaluation of the two most importantcomponents of glycemic control in individuals with diabetes: HbA_(1c)and the risk of hypoglycemia. The method, while using only routine SMBGdata, provides, among other things, three sets of output.

The potential implementations of the method, system, and computerprogram product of the invention is that it provides the followingadvantages, but are not limited thereto. First, the invention enhancesexisting home BG monitoring devices by producing and displaying: 1)estimated categories for HbA_(1c), 2) estimated probability for SH inthe subsequent six months, and 3) estimated short-term risk ofhypoglycemia (i.e. for the next 24 hours). The latter may includewarnings, such as an alarm, that indicates imminent hypoglycemicepisodes. These three components can also be integrated to providecontinuous information about the glycemic control of individuals withdiabetes, and to enhance the monitoring of their risk of hypoglycemia.

As a second advantage, the invention enhances existing software orhardware that retrieves SMBG data. Such software or hardware is producedby virtually every manufacturer of home BG monitoring devices and iscustomarily used by patients and health care providers to interpret SMBGdata. The methods and system of the invention can be directlyincorporated into existing home blood glucose monitors, or used for theenhancement of software that retrieves SMBG data, by introducing a datainterpretation component capable of predicting both HbA_(1c) and periodsof increased risk of hypoglycemia.

Still yet another advantage, the invention evaluates the accuracy ofhome BG monitoring devices, both in the low and high BG ranges, and overthe entire BG scale.

Moreover, another advantage, the invention evaluates the effectivenessof various treatments for diabetes.

Further still, as patients with diabetes face a life-long optimizationproblem of maintaining strict glycemic control without increasing theirrisk of hypoglycemia, the present invention alleviates this relatedproblem by use of its simple and reliable methods, i.e., the inventionis capable of evaluating both patients' glycemic control and their riskof hypoglycemia, and at the same time applying it in their everydayenvironments.

Additionally, the invention provides the missing link by proposing threedistinct, but compatible, algorithms for evaluating HbA_(1c) and therisk of hypoglycemia from SMBG data, to be used to predict theshort-term and long-term risks of hypoglycemia, and the long-term riskof hyperglycemia.

Finally, another advantage, the invention evaluates the effectiveness ofnew insulin or insulin delivery devices. Any manufacturer or researcherof insulin or insulin delivery devices can utilize the embodiments ofthe invention to test the relative success of proposed or tested insulintypes or device delivery designs.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The foregoingembodiments are therefore to be considered in all respects illustrativerather than limiting of the invention described herein. Scope of theinvention is thus indicated by the appended claims rather than by theforegoing description, and all changes which come within the meaning andrange of equivalency of the claims are therefore intended to be embracedtherein.

1-21. (canceled)
 22. A computerized method for evaluating the long termprobability for severe hypoglycemia (SH) of a patient based on BG datacollected over a predetermined duration, said method comprising:computing weighted deviation toward low blood glucose (WL) and estimatedrate of fall of blood glucose in the low BG range (DrDn) based on saidcollected BG data; and estimating the number of future SH episodes usinga predetermined mathematical formula based on said computed WL and DrDn.23. The method of claim 22, wherein: said computed WL is mathematicallydefined from a series of BG readings x₁, x₂, . . . x_(n) taken at timepoints t₁, t₂, . . . , t_(n) as:${WL} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\quad{{wl}\quad\left( {x_{i};2} \right)}}}$where: wl(BG;a)=10.f(BG)^(a) if f(BG)>0 and 0 otherwise, a=2,representing a weighting parameter, and said computed DR ismathematically defined as:DrDn=average of s_(k+1)−s_(k), provided that s_(k)<s_(k+1), where:s_(k)=10.S(k+t₁)² for k=0, 1, . . . , t_(n)−t₁, S(t_(j))=f(x_(j)), forj=1, . . . , n.
 24. The method of claim 22, wherein said estimatednumber of future SH episodes (EstNSH) is mathematically defined as:EstNSH=3.3613(WL)−4.3427(DrDn)−1.2716.
 25. The method of claim 22,further comprising: defining predetermined EstNSH categories, each ofsaid EstNSH categories representing a range of values for EstNSH; andassigning said EstNSH to at least one of said EstNSH categories.
 26. Themethod of claim 25, wherein said EstNSH categories are defined asfollows: category 1, wherein said EstNSH category is less than about0.775; category 2, wherein said EstNSH category is between about 0.775and about 3.750; category 3, wherein said EstNSH category is betweenabout 3.750 and about 7.000; and category 4, wherein said EstNSHcategory is above about 7.0.
 27. The method of claim 26, furthercomprising: defining a probability of incurring a select number of SHepisodes respectively for each of said assigned EstNSH categories;wherein said probability and said respective select number of SH aredefined as: said classified category 1 corresponds with about a 90%probability of incurring about 0 SH episodes and about a 10% probabilityof incurring about 1 or more SH episodes over the predeterminedduration; said classified category 2 corresponds with about a 50%probability of incurring about 0 SH episodes, 25% probability ofincurring about 1 to about 2 SH episodes, and 25% probability ofincurring more than 2 SH episodes over the predetermined duration; saidclassified category 3 corresponds with about a 25% probability ofincurring about 0 SH episodes, 25% probability of incurring about 1 toabout 2 SH episodes, and 50% probability of incurring more than 2 SHepisodes over the predetermined duration; and said classified category 4corresponds with about a 20% probability of incurring about 0 to about 2SH episodes and about a 80% probability of incurring more than 2 SHepisodes over the predetermined duration.
 28. The method of claim 25,further comprising: defining a probability of incurring a select numberof SH episodes respectively for each of said assigned EstNSH categories;and providing at least one probability of incurring a select number ofSH episodes according to said EstNSH category to which said EstNSH isassigned.
 29. A computerized method for evaluating the long termprobability for severe hypoglycemia (SH) of a patient based on BG datacollected over a predetermined duration, said method comprising:computing weighted deviation toward low blood glucose (WL) and estimatedrate of fall of blood glucose in the low BG range (DrDn) based on saidcollected BG data; estimating the number of future SH episodes using apredetermined mathematical formula based on said computed WL and DrDn;and defining a probability of incurring a select number of SH episodesrespective to said estimated SH episodes.
 30. A system for evaluatingthe long term probability for severe hypoglycemia (SH) of a patientbased on BG data collected over a predetermined duration, said systemcomprising: a database component operative to maintain a databaseidentifying said BG data; a processor programmed to: computing weighteddeviation toward low blood glucose (WL) and estimated rate of fall ofblood glucose in the low BG range (DrDn) based on said collected BGdata; and estimating the number of future SH episodes using apredetermined mathematical formula based on said computed WL and DrDn.31. The system of claim 30, wherein: said computed WL is mathematicallydefined from a series of BG readings x₁, x₂, . . . x_(n) taken at timepoints t₁, t₂, . . . , t_(n) as:${WL} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\quad{{wl}\quad\left( {x_{i};2} \right)}}}$where: wl(BG;a)=10.f(BG)^(a) if f(BG)>0 and 0 otherwise, a=2,representing a weighting parameter, and said computed DR ismathematically defined as:DrDn=average of s _(k+1) −s _(k), provided that s _(k) <s _(k+1), where:s_(k)=10.S(k+t₁)² for k=0, 1, . . . , t_(n)−t₁, S(t_(j))=f(x_(j)), forj=1, . . . , n.
 32. The system of claim 30, wherein said estimatednumber of future SH episodes (EstNSH) is mathematically defined as:EstNSH=3.3613(WL)−4.3427(DrDn)−1.2716.
 33. The system of claim 30,wherein said processor being further programmed to: define predeterminedEstNSH categories, each of said EstNSH categories representing a rangeof values for EstNSH; and assign said EstNSH to at least one of saidEstNSH categories.
 34. The system of claim 33, wherein said EstNSHcategories are defined as follows: category 1, wherein said EstNSHcategory is less than about 0.775; category 2, wherein said EstNSHcategory is between about 0.775 and about 3.750; category 3, whereinsaid EstNSH category is between about 3.750 and about 7.000; andcategory 4, wherein said EstNSH category is above about 7.0.
 35. Themethod of claim 34, wherein said processor being further programmed to:define a probability of incurring a select number of SH episodesrespectively for each of said assigned EstNSH categories, wherein saidprobability and said respective select number of SH are defined as: saidclassified category 1 corresponds with about a 90% probability ofincurring about 0 SH episodes and about a 10% probability of incurringabout 1 or more SH episodes over the predetermined duration; saidclassified category 2 corresponds with about a 50% probability ofincurring about 0 SH episodes, 25% probability of incurring about 1 toabout 2 SH episodes, and 25% probability of incurring more than 2 SHepisodes over the predetermined duration; said classified category 3corresponds with about a 25% probability of incurring about 0 SHepisodes, 25% probability of incurring about 1 to about 2 SH episodes,and 50% probability of incurring more than 2 SH episodes over thepredetermined duration; and said classified category 4 corresponds withabout a 20% probability of incurring about 0 to about 2 SH episodes andabout a 80% probability of incurring more than 2 SH episodes over thepredetermined duration.
 36. The system of claim 33, wherein saidprocessor being further programmed to: define a probability of incurringa select number of SH episodes respectively for each of said assignedEstNSH categories; and provide at least one probability of incurring aselect number of SH episodes according to said EstNSH category to whichsaid EstNSH is assigned.
 37. A glycemic control system for evaluatingthe long term probability for severe hypoglycemia (SH) of a patient,said system comprising: a BG acquisition mechanism, said acquisitionmechanism configured to acquire BG data from the patient, a databasecomponent operative to maintain a database identifying said BG data; aprocessor programmed to: computing weighted deviation toward low bloodglucose (WL) and estimated rate of fall of blood glucose in the low BGrange (DrDn) based on said collected BG data; and estimating the numberof future SH episodes using a predetermined mathematical formula basedon said computed WL and DrDn.
 38. A computer program product comprisinga computer useable medium having computer program logic for enabling atleast one processor in a computer system to evaluate long termprobability for severe hypoglycemia (SH) of a patient based on BG data,said computer program logic comprising: computing weighted deviationtoward low blood glucose (WL) and estimated rate of fall of bloodglucose in the low BG range (DrDn) based on said collected BG data; andestimating the number of future SH episodes using a predeterminedmathematical formula based on said computed WL and DrDn.
 39. Thecomputer program product of claim 38, wherein said computer programlogic further comprises: defining a probability of incurring a selectnumber of SH episodes respective to said estimated SH episodes.
 40. Acomputerized method for evaluating the short term risk for severehypoglycemia (SH) of a patient based on BG data collected over apredetermined duration, said method comprising: computing weighteddeviation toward low blood glucose (WL); determining Max(wl) bycalculating maximum value of wl(BG;2); and determining risk value bytaking the geometric mean of WL and Max(wl) over said predeterminedduration, said risk value is mathematically defined as:risk value=√{square root over (WL·Max(wl))}.
 41. The method of claim 40,wherein: said computed WL is mathematically defined from a series of BGreadings x₁, x₂, . . . x_(n) taken over the predetermined duration as:${WL} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\quad{{wl}\quad\left( {x_{i};2} \right)}}}$where: wl(BG;a)=10.f(BG)^(a) if f(BG)>0 and 0 otherwise, a=2,representing a weighting parameter.
 42. The method of claim 40, furthercomprising: providing a predetermined threshold risk value; andcomparing said determined risk value to said threshold risk value. 43.The method of claim 42, wherein: if said determined risk value isgreater than said threshold value then short term risk of incurring ahypoglycemic episode is high; and if said determined risk value is lessthan said threshold value then short term risk of incurring ahypoglycemic episode is low.
 44. The method of claim 43, wherein saidshort term is approximately a 24 hour period.
 45. The method of claim43, wherein said short term ranges from about 12 to about 72 hourperiod.
 46. The method of claim 43, wherein said threshold value isapproximately
 17. 47. The method of claim 43, wherein said thresholdvalue is between about 12 to
 25. 48. A system for evaluating the shortterm risk for severe hypoglycemia (SH) of a patient based on BG datacollected over a predetermined duration, said system comprising: adatabase component operative to maintain a database identifying said BGdata; a processor programmed to: compute weighted deviation toward lowblood glucose (WL); determine Max(wl) by calculating maximum value ofwl(BG;2); and determine risk value by taking the geometric mean of WLand Max(wl) over said predetermined duration, said risk value ismathematically defined as:risk value=√{square root over (WL·Max(wl))}.
 49. The system of claim 48,wherein: said computed WL is mathematically defined from a series of BGreadings x₁, x₂, . . . x_(n) taken over the predetermined duration as:${WL} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\quad{{wl}\quad\left( {x_{i};2} \right)}}}$where: wl(BG;a)=10.f(BG)^(a) if f(BG)>0 and 0 otherwise, a=2,representing a weighting parameter.
 50. The system of claim 48, whereinsaid processor being further programmed to: provide a predeterminedthreshold risk value; and compare said determined risk value to saidthreshold risk value.
 51. The system of claim 50, wherein: if saiddetermined risk value is greater than said threshold value then shortterm risk of incurring a hypoglycemic episode is high; and if saiddetermined risk value is less than said threshold value then short termrisk of incurring a hypoglycemic episode is low.
 52. The system of claim51, wherein said short term is approximately a 24 hour period.
 53. Themethod of claim 51, wherein said short term ranges from about 12 toabout 72 hour period.
 54. The system of claim 51, wherein said thresholdvalue is approximately
 17. 55. The method of claim 51, wherein saidthreshold value is between about 12 to
 25. 56. A glycemic control systemfor evaluating the short term risk for severe hypoglycemia (SH) of apatient, said system comprising: a BG acquisition mechanism, saidacquisition mechanism configured to acquire BG data from the patient, adatabase component operative to maintain a database identifying said BGdata; a processor programmed to: compute weighted deviation toward lowblood glucose (WL); determine Max(wl) by calculating maximum value ofwl(BG;2); and determine risk value by taking the geometric mean of WLand Max(wl) over said predetermined duration, said risk value ismathematically defined as:risk value=√{square root over (WL·Max(wl))}.
 57. A computer programproduct comprising a computer useable medium having computer programlogic for enabling at least one processor in a computer system toevaluate the short term risk for severe hypoglycemia (SH) of a patientbased on BG data collected over a predetermined duration, said computerprogram logic comprising: computing weighted deviation toward low bloodglucose (WL); determining Max(wl) by calculating maximum value ofwl(BG;2); and determining risk value by taking the geometric mean of WLand Max(wl) over said predetermined duration, said risk value ismathematically defined as:risk value=√{square root over (WL·Max(wl))}.
 58. The computer programproduct of claim 57, wherein said computer program logic furthercomprises: providing a predetermined threshold risk value; and comparingsaid determined risk value to said threshold risk value.